This relates generally to generating so-called super-resolution images which are higher resolution images based on multiple lower resolution images.
In electronic imaging applications, images with higher resolution are more desirable. Images with higher resolution have greater pixel density and, hence, show greater detail than lower resolution images of the same scene. Higher resolution images have many applications, including medical imaging, satellite imaging, computer vision, video surveillance, face recognition, car plate number extraction and recognition, and converting digital versatile disk video to high density television, to mention a few examples.
In super-resolution image reconstruction, multiple observed lower resolution images or frames of a scene are used to create a higher resolution image. The lower resolution images may be different views of the same scene. They may be obtained from the same camera, for example, while introducing small, so-called sub-pixel shifts in the camera location from frame to frame, or capturing a small amount of motion in the scene. Alternatively, the low resolution images may be captured using different cameras aimed at the same scene. A resultant high resolution image is then reconstructed by aligning and properly combining the low resolution images so that additional image information is obtained. The process may also include image restoration, where de-blurring and de-noising operations are performed as well.
The reconstruction of the resultant high resolution image is a difficult problem because it belongs to the class of inverse, ill-posed mathematical problems. The needed signal processing may be interpreted as being the reverse of the so-called observation model, which is a mathematically deterministic way to describe the formation of low resolution images of a scene based upon known camera parameters. Since the scene is approximated by an acceptable quality high resolution image of it, the observation model is usually defined as relating a high resolution discrete image of the scene to its corresponding low resolution images. This relationship may be given as a concatenation of geometric transform, a blur operator, and down-sampling operator, plus an additive noise term.